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Sustainable Solutions

Using ANAI, citizen data scientists and other developers with limited machine learning expertise can easily ingest data, generate insights and build/train ML models.

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potential use cases

AI in Sustainable Solutions

Optimizing the grid

Optimize the electric and power supply network by analyzing the data on power demand variability, coal supply, and other interesting factors helping the grid to be more sustainable and less emissive.

Traffic management

Find new optimized routes to a location and also efficiently adjust the traffic lights to keep the vehicles constantly moving, to help decongest the cities using AI-based models.

Water monitoring

Manage and equip drought-prone regions better using AI and get help with water preservation, cleaning and identifying water scarcity to prepare for such calamities in advance.

Smart Agriculture

Help farmers manage large-scale farms for water-scarce regions and pest-infested zone identification in an autonomous way leading to a limited but proper use of pesticides and water.

limits

challenges found

01
The use of AI in achieving sustainability brings its own data science related challenges such as the quality of the data, setting up smart sensors, and building/training such AI models. 
02
The cost required to set up such systems becomes expensive and the trustability of such models is also questioned from time to time. 
our platform

With ANAI, Build The Best AI Solutions

  • ANAI allows an all-in-one platform to handle all the data science related workflow needs making the data management and the model building/training process easier.
  • ANAI replaces the need for setting up a huge team of ML experts hence saving costs and providing explainability within its platform to make the ML models more explainable and trustable.

sustainable Solutions and artificial intelligence | aI in sustainable Solutions | artificial intelligence sustainable Solutions

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